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Building, Analyzing, and Presenting a Dataset

Published onJun 23, 2020
Building, Analyzing, and Presenting a Dataset

Author & Project Role

Author: Marsely Kehoe, Director of the Mellon Scholars Program, Hope College

Roles: Instructor

Assignment Files

Learning Objectives

What did you want students to be able to do by completing this assignment?

Students should start to understand the many choices that go into assembling and structuring data, and how these choices affect the outcomes and visualizations produced by that data. Technologies are introduced or practiced including spreadsheets, several easy data visualizations, and a content management system, all of which can be scaled up for later projects.

Technology-Dependent Learning Outcomes

Was there anything this assignment taught students that you felt they wouldn't have been able to learn through other types of class assignments?

What is most satisfying about this project, from the instructor perspective, is the deeper discussion about data that develops naturally during this assignment, as students interrogate data-gathering practices, consider their own subjectivities as researchers, and debate the merits of a data-driven approach to the sometimes data-resistant objects they are working with.

Skill Level

What is the course title and level?

Sophomore seminar, though appropriate for beginners at all levels.

What kinds of prior knowledge is necessary to complete this assignment? How do students gain this knowledge?

No prior knowledge of technologies or research methods are required.

Assignment Description

This project introduces students to the fundamentals of creating a dataset, developing data visualizations (with Palladio, Voyant, and RAWGraphs), working with a content management system (Omeka), and digital imaging, and furthers written and oral presentation skills. Students work with a very small collection of objects (10 or 30), drawn from the physical holdings of the college (archival documents, rare books, artworks) or from social media. This is essentially a proof of concept, and a chance to learn and apply new software (all cost-free). Students can choose any topic they are interested in, and often choose unconventional and non-academic topics – the focus here is on process, so the content and findings are less central to the learning outcomes.

Time Needed

How much time did you allot to this project?

This project takes place over six weeks of the twice-weekly class, with the remainder of class time devoted to unrelated coursework.

  • 2.5 class periods for technology tutorials

    • digital imaging (half class)

    • data visualizations (Voyant, Palladio, RAWGraphs)

    • Omeka

  • 3 hands-on in-class work days

  • 1 presentation day

The remainder of work is an estimated 5-10 hours completed outside of class.

Support & Training

What kinds of support or training did you provide to help students learn to use new techniques or specialized tools?

I provide brief in-class tutorials for Omeka, Voyant, Palladio, and RAWGraphs (slides originally developed by a former course TA, Taylor Elise Mills), tools which I am not expert in, and for which plenty of resources exist.


Did you need any specialized equipment, tools, or human resources to make this assignment feasible? If so, please describe.

I use the free version of all software, and students use their own laptops, and the spreadsheet software of their choice. Students have the option to scan items in the library (there is a large-scale book scanner) or rent cameras, but most use screenshots or their phones. Curators of the various collections provide some guidance, but this would vary depending on the local environment.


How did you assess or grade this project?

There is a rubric (available on assignment sheet above); most grading took place at the end but there were many opportunities for comments and check-ins with in-class tutorials and work days.

Challenges & Opportunities

If you assigned this project again, would you change anything? If so, what?


Describe any trouble spots or complications someone else might want to be aware of before trying a similar assignment in a course of their own.

Students get frustrated with the small scale of the project (because their data visualizations can be awkward or insignificant), but small-scale is key to flexibility and refinement of the dataset. Depending on the data, each student project won't work equally well with all three data viz applications (RAWGraphs is especially picky), but I tell them to keep trying, and if they fail, they can explain why their visualization is not helpful.

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